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import os

import os.path as op

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as sp

from myst_nb import glue 
from IPython.display import HTML

import sys
sys.path.append("../../../functions")
from data_downloaders import download_HOT_CO2_data, download_MLO_CO2_data

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting import plot_timeseries
from ind_setup.plotting_int import plot_timeseries_interactive, fig_int_to_glue
from ind_setup.tables import plot_df_table

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 16
     14 import sys
     15 sys.path.append("../../../functions")
---> 16 from data_downloaders import download_HOT_CO2_data, download_MLO_CO2_data
     18 sys.path.append("../../../../indicators_setup")
     19 from ind_setup.plotting import plot_timeseries

ModuleNotFoundError: No module named 'data_downloaders'

update_data = True
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

url = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt'

if update_data:
    MLO_data = download_MLO_CO2_data(url)
    MLO_data.to_pickle(op.join(path_data, 'MLO_co2.pkl'))
else:
    MLO_data = pd.read_pickle(op.join(path_data, 'MLO_co2.pkl'))    

MLO_data['CO2_deseasoned'] = MLO_data['CO2'].rolling(window=12, center=True).mean()
glue("n_years", len(np.unique(MLO_data.index.year)), display=False)
glue("co2_2024", int(MLO_data.loc['2024'].CO2.mean()), display=False)
glue("start_year", MLO_data.dropna().index[0].year, display=False)

dict_plot = [{'data' : MLO_data, 'var' : 'CO2', 'ax' : 1, 'trendline' : True, 'label' : 'MLO: CO2'},
             {'data' : MLO_data, 'var' : 'CO2_deseasoned', 'ax' : 1, 'trendline' : False, 'label' : 'MLO: CO2 (Deseasoned)'}]

fig, trend = plot_timeseries_interactive(dict_plot, trendline=True, figsize=(20, 10), return_trend=True);
fig.write_html(op.join(path_figs, 'F1_CO2_trend.html'), include_plotlyjs="cdn")


glue("trend", float(trend[0]), display=False)
glue("change", float(trend[0]*len(np.unique(MLO_data.index.year))), display=False)
glue("trend_fig", fig_int_to_glue(fig), display=False)

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MLOy = MLO_data[['CO2']].groupby(MLO_data.index.year).mean()
MLOy.index = pd.to_datetime(MLOy.index, format = '%Y')

d = MLOy.describe()
d.loc['Rate of change'] = trend
fig = plot_df_table(np.round(d, 2), figsize = (400, 400))